Multi-sensor quality inference and control for additive manufacturing processes

ABSTRACT

This invention teaches a multi-sensor quality inference system for additive manufacturing. This invention still further teaches a quality system that is capable of discerning and addressing three quality issues: i) process anomalies, or extreme unpredictable events uncorrelated to process inputs; ii) process variations, or difference between desired process parameters and actual operating conditions; and iii) material structure and properties, or the quality of the resultant material created by the Additive Manufacturing process. This invention further teaches experimental observations of the Additive Manufacturing process made only in a Lagrangian frame of reference. This invention even further teaches the use of the gathered sensor data to evaluate and control additive manufacturing operations in real time.

CROSS-REFERENCES TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.14/945,247 filed Nov. 18, 2015, now U.S. Pat. No. 10,786,948 issued onSep. 29, 2020; which claims priority under 35 USC 119(e) to U.S.Provisional Patent Application No. 62/081,004 filed on Nov. 18, 2014,U.S. Provisional Patent Application No. 62/185,910 filed on Jun. 29,2015, and to U.S. Provisional Application No. 62/235,232 filed on Sep.30, 2015. The disclosures of which are incorporated by reference intheir entirety and for all purposes.

BACKGROUND OF THE INVENTION

Additive manufacturing, or the sequential assembly or construction of apart through the combination of material addition and applied energy,takes on many forms and currently exists in many specificimplementations and embodiments. Additive manufacturing can be carriedout by using any of a number of various processes that involve theformation of a three dimensional part of virtually any shape. Thevarious processes have in common the sintering, curing or melting ofliquid, powdered or granular raw material, layer by layer usingultraviolet light, high powered laser, or electron beam, respectively.Unfortunately, established processes for determining a quality of aresulting part manufactured in this way are limited. Conventionalquality assurance testing generally involves destruction of the part.While destructive testing is an accepted way of validating a part'squality, as it allows for close scrutiny of various internal portions ofthe part, such tests cannot for obvious reasons be applied to aproduction part. Consequently, ways of non-destructively verifying theintegrity of a part produced by additive manufacturing is desired.

SUMMARY

The described embodiments are related to a large subcategory of additivemanufacturing, which involves using an energy source that takes the formof a moving region of intense thermal energy. In the event that thisthermal energy causes physical melting of the added material, then theseprocesses are known broadly as welding processes. In welding processes,the material, which is incrementally and sequentially added, is meltedby the energy source in a manner similar to a fusion weld.

When the added material takes the form of layers of powder, after eachincremental layer of powder material is sequentially added to the partbeing constructed, the heat source melts the incrementally added powderby welding regions of the powder layer creating a moving molten region,hereinafter referred to as the weld pool, so that upon solidificationthey become part of the previously sequentially added and melted andsolidified layers below the new layer that includes the part beingconstructed. As additive machining processes can be lengthy and includeany number of passes of the weld pool, it can be difficult to avoid atleast slight variations in the size and temperature of the weld pool asthe weld pool is used to solidify the part. It should be noted thatadditive manufacturing processes are typically driven by one or moreprocessors associated with a computer numerical control (CNC) due to thehigh rates of travel of the heating element and complex patterns neededto form a three dimensional structure.

An additive manufacturing method is disclosed and includes thefollowing: monitoring the temperature of a first portion of a buildplane during an additive manufacturing operation with a first opticaltemperature sensor; monitoring the temperature of a second portion ofthe build plane that includes the first portion with a second opticaltemperature sensor; detecting a change in state of material within thefirst portion as a heat source passes through the first portion of thebuild plane with the first sensor; calibrating the second sensor bycorrelating the change in phase detected by the first sensor withinformation collected by the second sensor during the detected phasechange; and changing an amount of heat supplied by the heat source inaccordance with the calibrated temperature information provided by thesecond sensor.

An additive manufacturing system is disclosed and includes thefollowing: a processor; a heat source configured to direct energytowards a layer of powder arranged on a powder bed in a pattern definedby the processor that corresponds to a shape of a part; a first opticalsensor configured to determine a temperature associated with a fixedportion of the part; and a second optical sensor configured to receivelight emitted by a portion of the layer of powder being melted by theenergy from the heat source. The processor is configured to receivesensor data from the first and second optical sensors during an additivemanufacturing operation and to calibrate the sensor data by identifyingphase changes during an additive manufacturing operation.

An additive manufacturing method is disclosed and includes thefollowing: applying heat to a powder distributed across a powder bedusing a heat source; measuring an amount of heat being emitted by aportion of the powder bed with an optical temperature sensor;identifying the time at which the portion of the part undergoes meltingand solidifying phase change; calibrating the temperature data retrievedby the optical temperature sensor using the temperature at which thepart undergoes the melting phase change and the temperature at which thematerial making up the metal part is known to melt; and adjusting theamount of heat applied by the heat source in accordance with thecalibrated temperature data.

BRIEF DESCRIPTION OF THE DRAWINGS

The disclosure will be readily understood by the following detaileddescription in conjunction with the accompanying drawings, wherein likereference numerals designate like structural elements, and in which:

FIG. 1 is a schematic illustration of a Lagrangian path;

FIG. 2 is an illustration of a detailed way in which multi-sensorLagrangian data can be combined with models to predict processparameters in an Additive Manufacturing process that are not directlyexperimentally measured;

FIG. 3A is a schematic illustration of a system with an intense heatsource, in this specific instance taken to be a laser beam;

FIG. 3B is a schematic illustration of a system with an intense heatsource, in this specific instance taken to be an electron beam;

FIG. 4 is an illustration of a more specific correlation protocol bywhich the various measurements made in different frames of reference canbe cross-correlated;

FIG. 5 shows the resulting average photodiode signal intensity in aLagrangian reference frame for the process conditions shown in Table 1;

FIG. 6 shows the variation in photodiode Average Voltage signal withtravel speed at the power level of 146.4 W;

FIG. 7 shows a similar trend as FIG. 6 at 195 W;

FIG. 8 shows both nominal and off-nominal conditions during additivemanufacturing operations;

FIG. 9 shows a melt pool;

FIG. 10 is a similar illustration as FIG. 9 with false colorhighlighting;

FIG. 11 shows the dependence of oscillation frequency as a function ofmelt pool size for the primary or first mode of oscillation;

FIG. 12 shows the predicted cooling rate;

FIGS. 13A and 13B show experimental data derived from a Eulerian Sensor;

FIG. 14 shows specific scan cooling rate values on the vertical axis ofa 3D plot;

FIG. 15A shows an exemplary field of view of a pyrometer setup as aEulerian sensor;

FIG. 15B schematically shows the field of view of the pyrometer and thetrack of thermally affected and melted material caused by the movingheat source;

FIG. 15C shows an exemplary scan pattern that can be traversed by a heatsource;

FIGS. 15D-15E show various alternative scan patterns;

FIGS. 16A and 16B show raw data from a fixed (Eulerian) pyrometer;

FIGS. 17A shows a graph of spectral emissivity as a function oftemperature;

FIG. 17B shows a theoretical calculation of the emissivity of the ametal powder;

FIG. 17C shows a graph depicting a relationship between volume fractionporosity of the powder for powders having varying total solidemissivities;

FIG. 17D shows a graph depicting normalized energy density vstemperature;

FIG. 18 shows a flowchart describing in detail how thermal sensor datafrom various locations in the additive manufacturing build plane can beused;

FIGS. 19A-19F show how the processes described with regards to FIG. 18can be applied to real temperature readings

FIG. 20 shows the hierarchy of process control from the highest levelrequirements down to the lowest level real-time control loop;

FIG. 21 shows a block diagram reviewing the overall derivation ofquality specifications and categorizes, the resultant measurements thatmust be incorporated in order to verify adherence to overall qualityspecification and hence to the original design intent; and

FIG. 22 shows a high level process control flow chart.

DETAILED DESCRIPTION

In any process in which heat and mass are being transported, it ispossible to formulate the fundamental governing equations for theprocess in one of two reference frames: Eulerian or Lagrangian. AEulerian frame of reference specifies quantities associated with anytransport phenomenon by looking at values associated with specificpoints in space at given intervals in time. Therefore the point grid inspace is fixed, and the medium “flows” through this grid. This isanalogous to sitting on a riverbank and watching the river go by. ALagrangian frame of reference looks at physical quantities associatedwith the transport phenomenon along lines of flow in the flowfield or inthe medium itself, i.e. “moving with” the transport phenomenon. This canbe thought of as analogous to sitting in a boat and observing the riveras you are moving with the flow of the river. This can be schematicallydescribed as shown in FIG. 1. The specific line of flow 100 is a pathalong which specific physical quantities are to be evaluated in theLagrangian frame of reference. At a specific location x and time t 101,the specific attribute or physical quantity associated with thetransport phenomenon will have a certain value. At a later point x+Δxand time t+Δt, the attribute in question will have yet another value. Ifwe designate the given quantity being measured as α, and we write thescalar representation of the change alone any given direction xi thenthe change between these two material points is given by:

$\begin{matrix}{\frac{d\;\alpha_{P}}{dt} = {{\frac{\partial\alpha}{\partial t} + {( \frac{\partial\alpha}{\partial x_{i}} )( \frac{{dx}_{i}}{dt} )_{P}}} = {\underset{\underset{{Local}\mspace{14mu}{change}\mspace{14mu}{in}\mspace{14mu}{time}}{︸}}{\frac{\partial\alpha}{\partial t}} + \underset{\underset{{Change}\mspace{14mu}{in}\mspace{14mu}{space}}{︸}}{\frac{\partial\alpha}{\partial x_{i}}U_{i}}}}} & {{Eq}.\mspace{14mu}(1)}\end{matrix}$

Ui is the flow velocity along the i-th axis. However this is just thechange in the quantity as measured in the Eulerian reference frame. Thelocal change in time and space are in the Lagrangian reference frame.Therefore Eq. (1) links the change in any given quantity in the Eulerianreference frame to the change in that quantity in the Lagrangianreference frame.

With respect to Additive Manufacturing processes that involve thecreation of a molten pool, the cooling rate at the liquid-solidinterface, the thermal gradient at the liquid-solid interface, and thetravel velocity of the moving heat source are related by the followingequation:

$\begin{matrix}{\frac{\partial T}{\partial t} = {V\frac{\partial T}{\partial x}}} & {{Eq}.\mspace{14mu}(2)}\end{matrix}$

Where

$\frac{\partial T}{\partial t}$is the cooling rate at the liquid-solid interface, V is the travelvelocity of the moving heat source, and

$\frac{\partial T}{\partial x}$is the thermal gradient at the liquid solid interface. It is furtherassumed that the heat source is moving in the x-direction, or that thechoice of direction for the x-axis is chosen such that it is alignedwith the direction of movement for the heat source. Furthermore, in manyinstances associated with Additive Manufacturing, especially thoseprocesses involving powder beds that are melted or sintered by movingheat sources, it is found that the thermal gradients are very steep andare typically confined to a region that is immediately adjacent to themelt pool. When we were to estimate the energy source used in AdditiveManufacturing as a point source, then the equations for the thermalfield are analytically tractable assuming constant material properties.In the case of a point heat source moving in the x-direction impingingon a semi-infinite solid, the temperature field is given by theexpression shown below. This is within the reference frame of the beam.

$\begin{matrix}{{T - T_{0}} = {\frac{P}{2\pi{kR}}\exp\{ {- \frac{V( {R - x} )}{2\alpha}} \}}} & {{Eq}.(3)}\end{matrix}$

Where R is the distance to the heat source, P is the beam power, V isthe travel speed in the x-direction, k is the thermal conductivity, T₀is the ambient temperature of the powder bed, and a is the thermaldiffusivity.

Now if the reference frame is changed to assume that the measurement isbeing made in the material as the beam is moving past the measurementpoint, then the approximate relationship describing the time history ofthe thermal profile is described by:

$\begin{matrix}{{T - T_{0}} = {{\frac{p}{2\pi{kVt}} \cdot \exp}\{ {- \frac{r^{2}}{4\alpha t}} \}}} & {{Eq}.(4)}\end{matrix}$

Where r is now the distance to the centerline of the melt pass, P is thebeam power, V is the travel speed in the x-direction, k is the thermalconductivity, T₀ is the ambient temperature of the powder bed and a isthe thermal diffusivity. If we differentiate Eq. (4) and set thederivative equal to zero, an expression for the maximum temperature canbe derived:

$\begin{matrix}{{T_{MAX} - T_{0}} = \frac{2\alpha P}{\pi{ekVr}^{2}}} & {{Eq}.(5)}\end{matrix}$

Where r is the distance to the centerline of the melt pass, P is thebeam power, V is the travel speed in the x-direction, k is the thermalconductivity, T₀ is the ambient temperature of the powder bed and a isthe thermal diffusivity.

Alternatively, if we differentiate Eq. (3) with respect to time and onlyconsider points along the melt centerline, i.e. r=0, then afterrearranging the terms by applying Eq. (4) again to replace time as avariable, we get:

$\begin{matrix}{\frac{\partial T}{\partial t} = {{- \frac{2\pi{kV}}{P}} \cdot ( {T - T_{0}} )^{2}}} & {{Eq}.(6)}\end{matrix}$

Now it is instructive to consider a few characteristic times for theAdditive

Manufacturing process especially when conducted by a moving intense heatsource. The characteristic beam interaction time is the timescale overwhich the moving heat source or beam interacts with any given region ofmaterial, and it is given by:

$\begin{matrix}{\tau_{BEAM} = \frac{D}{V}} & {{Eq}.(7)}\end{matrix}$

Where D is the beam diameter and V is the travel speed of the beam. Thecharacteristic heat conduction time is the time for heat to conduct awayfrom the molten region. Typically in Additive Manufacturing involvingpowder beds, the thickness of the layer being deposited is less than thebeam diameter, and is also less than the melt pool diameter. However thebeam melts more than just the layer being deposited, and metallurgicalevidence indicates that the melt pool has roughly a 1 to 1 aspect ratioin terms of width to depth. Therefore the characteristic distance overwhich heat conduction should be evaluated is roughly the beam diameteras well. In this case, the characteristic heat conduction time is thengiven by:

$\begin{matrix}{\tau_{COND} = \frac{D^{2}}{4\alpha}} & {{Eq}.(8)}\end{matrix}$

Where D is the beam diameter and a is the thermal diffusivity. It shouldbe noted that in some cases the heat source can have other shapes andthe use of a circular beam geometry for a heat source should not beconstrued as limiting. For many cases of practical interest, the beamdiameter is on the order of 100 microns, the travel speed is on theorder of 1 m/s, and the thermal diffusivity is on the order of 1−5×10⁻⁶m²/s. For alloys like aluminum alloys this will be higher possibly by anorder of magnitude, but for steels, titanium alloys, and nickel alloysthe thermal diffusivity is lower. Under these conditions, thecharacteristic beam interaction time 10⁻⁴ sec., and the characteristicheat conduction time is 5 to 10 times as long. Therefore there will besuperheat in the liquid as well as a thermal gradient between the centerand the edge of the melt pool. These conditions will drive what is knownas a Marangoni flow, or a flow which is driven by surface tensiongradients as the surface tension strongly depends on the temperature,and in the case of such small melt pools the surface tension willdominate any inertial forces.

However there are several factors which will counteract the tendency forthe pool to overheat or to maintain very large thermal gradients.Firstly, any overheating will drive evaporation, and given that thelatent heat of evaporation of is very large for most metals, this willhave a powerful cooling effect. For example in a 100 micron diametermelt pool in steel, evaporation of just 1% of the mass of the melt poolwill cool the pool by more than 5%. Therefore evaporation will be apowerful mechanism by which overheating is avoided in practice.Additionally, the Marangoni flows will serve to lessen thermal gradientsthrough convection, although if thermal gradients disappear entirelythen the Marangoni flow itself stops. Lastly from a practicalmeasurement perspective, it is difficult to get a non-imaging thermalsensor which will have a spot size less than 100 microns and still havea large standoff distance to enable a remote measurement. The net resultof all of these physical phenomena as well as measurement limitations isthat the following relationship exists between the observed temperaturein a Lagrangian (beam-following) reference frame and the actual peaktemperature of the melt pool which will hold roughly over the diameterof the melt pool:T _(OBS) =K·T _(MAX)   Eq. (9)

Or alternatively:T _(MAX) =K′·T _(OBS)   Eq. (10)

Where T_(MAX) is the peak temperature, T_(OBS) is the observedtemperature as seen by a non-contact sensor in the frame of reference ofthe beam (Lagrangian frame), and K or K′ are constants that depend onemissivity, the field of view of the temperature sensor, and otheroptical properties of the measurement that could cause attenuation andlosses such as lenses, mirrors, etc.

Now let T* be defined as:T*≡(T−T ₀)   Eq. (11)

Then:T* _(MAX)=(T _(MAX) −T ₀)   Eq. (12)

Then by invoking Eq. 10, this becomes:T* _(MAX)=(K′·T _(OBS) −T ₀)   Eq. (13)

And by further invoking Eq. 4 this becomes:

$\begin{matrix}{T_{MAX}^{*} = {( {{K^{\prime} \cdot T_{OBS}} - T_{0}} ) = \frac{2\alpha P}{\pi{ekVr}^{2}}}} & {{Eq}.(14)}\end{matrix}$

And since this holds over roughly the region of the meltpool, we cansubstituted d/2 for r, where d is the melt pool diameter:

$\begin{matrix}{T_{MAX}^{*} = {( {{K^{\prime} \cdot T_{OBS}} - T_{0}} ) = \frac{8\alpha P}{\pi{ekVd}^{2}}}} & {{Eq}.(15)}\end{matrix}$

Now with respect to the cooling rate, it is possible to combine Eq. 13and Eq. 6 to estimate the cooling rate associated with the melt poolalong the centerline (r=0) and at the trailing edge of the melt pool,i.e. the liquid/solid boundary that trails with respect to the motion ofthe heat source:

$\begin{matrix}{\frac{\partial T}{\partial t} = {{{- \frac{2\pi{kV}}{P}} \cdot ( {{K^{\prime} \cdot T_{OBS}} - T_{0}} )^{2}} = {{- \frac{2\pi{kV}}{P}} \cdot ( T_{MAX}^{*} )^{2}}}} & {{Eq}.(16)}\end{matrix}$

So Eq. (15) and Eq. (16) allow measurements of peak temperature from anon-imaging sensor in the Lagrangian frame of reference to be combinedwith known values of power and travel speed to be used to calculatecooling rate as well as melt pool size, for example. This is just oneexample of such a models-based inference that combines real processmeasurements with models to predict and infer quantities that areotherwise difficult to measure in the Lagrangian frame of reference.Other examples of models could include: finite element models, finitedifference models, lookup tables based on either empirical data orcompiled results of analytical models, neural networks, expert systems,databases, or any other parametric or heuristic methodology which allowscorrelations to be made between variables and inferences to be madeabout quantities not directly experimentally observed.

FIG. 2 is an illustration of a detailed way in which multi-sensorLagrangian data can be combined with models to predict processparameters in an Additive Manufacturing process that are not directlyexperimentally measured. The Additive Manufacturing process 200 issensed by a variety of sensors 201 in the Lagrangian frame of reference.Based on this experimental data and with the aid of process models aspreviously described above, the real-time multi-sensor Lagrangian datais used to predict quantities or parameters 202 which are not possibleto directly measure in the Lagrangian frame of reference, such as thecooling rate as indicated above. In parallel, non-Lagrangian data 203such as Eulerian thermal measurements, or metallurgical data such asmelt pool size measurement directly from metallurgical cross sections,are collected. This non-Lagrangian data 203 is used for the purposes ofvalidating the Multi-sensor Lagrangian data 201. A cross-correlation 204is performed using a variety of statistical or non-statistical methods.The purpose of this cross-correlation 204 is to validate the ability ofthe Lagrangian multi-sensor data 201 in conjunction with the modelpredictions 202 to accurately predict process parameters not directlymeasured within the Lagrangian reference frame. Once thiscross-correlation 204 is successfully demonstrated, then the Lagrangiandata 201 alone becomes an accurate predictor of process quality 205.This is a beneficial characteristic of some embodiments of the presentinvention.

One specific method by which the cross-correlation 204 can be achievedis now discussed. First, the features associated with a baselinecondition are identified as one set of Lagrangian data 201. Then thefeatures from any given test case are compared to the baseline conditionas follows. First the features from the baseline case are averaged and avector of the mean of these features M is created. The test vector X hasthe same dimensionality as the vector of feature means because it hasthe same number of features, which will be also called the degrees offreedom. Then the classification scheme taught in this present inventioninvolves the use of the Mahalanobis distance, which is simply given by:MD ² =[X−M] ^(T) ·COV _(x) ·[X−M]  (17)

Where COV_(X) is the covariance matrix of X. It can be shown that whenthe features are normally distributed, then the square of the MDdistance will be Chi-Square distributed. The Chi-Squared probabilitydensity distribution is given by:

$\begin{matrix}{{f( {x;k} )} = \{ \begin{matrix}{\frac{x^{{({k/2})} - 1}e^{{- x}/2}}{2^{k/2{\Gamma(\frac{K}{2})}}},} & {{x\underset{.}{\geq}0};} \\{0,} & {{otherwise}.}\end{matrix} } & {{Eq}.(18)}\end{matrix}$

Where Γ is the Gamma Function and k is the number of degrees of freedom,which in this case is identical to the number of features. The criticalvalue of the Chi-Squared distribution at a given confidence level and agiven number of degrees of freedom can be calculated. This is athreshold value of the distribution above which a point could beconsidered as an outlier within the context of fitting the MD Distance ta Chi-Squared distribution. For example, at a 95% confidence level, or acritical p-value of 0.05, the corresponding table of critical values ofthe Chi-Squared distribution and therefore the MD distance squared aswell are given by the following table:

TABLE 1 CRITICAL VALUES OF CHI-SQUARED DISTRIBUTION Degrees of FreedomCritical Value of the Chi-Squared (also the number of FeaturesDistribution - also critical value of in the Feature Vector) the squareof the MD distance 1 3.84 2 5.99 3 7.82 4 9.49 5 11.07 6 12.59 7 14.07 815.51 9 16.92 10 18.31Multi-Sensor Lagrangian Data Collection

FIGS. 3A-3B show various configurations of a multi-sensor Lagrangianprocess sensing scheme. It is instructive to examine precisely in whatmanner the Lagrangian data can be acquired so that in combination withmodels and a cross-correlation technique as described above, theLagrangian data can become predictive of process features and attributesthat would otherwise only be observable in a Eulerian reference frame.In FIG. 3, the intense heat source 300 is in this specific instancetaken to be a laser. The beam 301 emitted by heat source 300 originatesat the laser head and passes through a partially reflective optic 302.This optic 302 is designed to be essentially fully transmissive at thespecific wavelength that the laser operates, and reflective at otheroptical wavelengths. Generally the laser wavelength will be infrared ornear-infrared, or typically wavelengths of 1000 nm or greater. The lasercan include a scanning head 303 that consists of x and y positioninggalvanometers as well as a focus lens, such as an f-theta lens. The beam301 is therefore focused and strikes the workpiece 304 at a givenlocation 305 thus generating a molten region on the workpiece 304. Theheated region results in optical radiation 306 being emittedisotropically and uniformly over a large solid angle. Some of thisoptical radiation 306 will make its way back through the scanning head303 and is reflected by the partially reflective optic 302.

This reflected optical beam 307 then makes its way through a series ofanalytical instruments. A beam splitter 308 sends a portion of the beamto a photodiode 309. Photodiode 309 can be capable of sensing a range offrequencies at a high enough speed and recording rate to detect possibleanomalies occurring during a deposition process, i.e. sudden departuresfrom an average or mean intensity level. The remaining portion of thereflected optical beam 307 then goes to another beam splitter 310 and aportion of this beam is collected by a pyrometer 311. The pyrometer 311may integrate this signal over some time interval in order to assign atemperature to the light thus collected. The signal should be correctedfor the various optical attenuations that have occurred through beamsplitting as well as the correction for the remote location of themolten region 305 on the workpiece 304 that resulted in the opticalemission 306 of which a portion 307 was collected. Lastly the remainingportion of the reflected optical beam 307 is directed by a mirror 312into a high speed optical imaging sensor 313 which could be a camera, orsome other kind of linear or area CCD array or other imaging array. Thisoptical imaging sensor 313 captures a 1D or 2D image that correlates tothe size of the molten region. By using a relatively low resolutionsensor 313, sensor 313 can be configured to record data at extremelyhigh frame rates, so that sensor 313 is capable of detecting verytransient temperature excursions occurring during a build process.

In addition to the various sensors in the Lagrangian reference frame, acritical part of this present invention is the existence of at least oneor some small set of measurements made in an Eulerian reference framethat is completely independent of the Lagrangian reference frame. ThisEulerian measurement is used for correlation and calibration purposes.For example in FIG. 3, a stationary pyrometer 314 in the Eulerianreference frame independently measures the temperature and thereforeprovides a calibration to the measurement made by the Lagrangianpyrometer 311. The field of view 315 of the stationary Eulerianpyrometer 314 is suitably chosen so that it matches the characteristicdimension of the molten zone 305 existing on the workpiece 304 and madeby the focused laser beam 301 at the specific location to which thescanning head 303 displaced and focused the beam 301.

In addition to the aforementioned sensors, additional sensors can beadded to enhance measurements taken by the additive manufacturing sensorsystem. Device 316 can be a part of a mechanism that distributes layersof powder across a top surface of workpiece 304. Device 316 can includea contact sensor 318 that is configured to measure any disruptions tothe spreading of the powder such as vibrations or jolts likely to resultin an uneven application of the powder.

In some embodiments, sensing of a vibration of device 316 can be used toaccurately predict changes to the powder layer. The depicted sensingsystem can also include an acoustic sensor 320. Acoustic sensor 320 canbe arranged along one side of the build platform so that as workpiece304 is built up acoustic sensor 320 can be configured to listen for theformation of micro-cracks within workpiece 304. Acoustic sensor 320 canbe calibrated to determine various characteristics of micro-crackingwithin workpiece 304. Micro-cracking can be caused from many things butin particular from improper cooling rates. FIG. 3A also shows a Eulerianphotodiode 322 having a field of view 324, which can be configured todetect temperature changes in substantially any portion of the topsurface of workpiece 304. In some embodiments, pyrometer 314 can beconfigured to provide calibration information to Eulerian photodiode322, thereby allowing Eulerian photodiode 322 to accurately distinguishthe temperature of any point on the top surface of workpiece 304.

FIG. 3B shows an alternative embodiment in which an electron beam isutilized to melt materials as part of an additive manufacturing process.In particular, FIG. 3B shows electron beam system including electronbeam gun 350, which generates an electron beam 351 that is focused bythe electromagnetic focusing system 352 and is then defected by theelectromagnetic deflection system 353 resulting in a finely focused anddeflected electron beam 354, which creates a hot beam-materialinteraction zone 355 on the workpiece 356. Optical energy is emitted 358which could be collected by a series of sensors 359 each with their ownrespective field of view 360 which again could be locally isolated tothe interaction region 355 or could encompass the entire build area 356.Additionally, the sensors 359 could have their own optical tracking andscanning system which could follow the electron beam 354 as it movesacross the build area 356. Whether or not these sensors 359 have opticaltracking or not, the sensors 359 could consist of pyrometers,photodiodes, spectrometers, and high or low speed cameras operating inthe visible or infrared (IR) spectral regions. The sensors 359 couldalso be sensors which combine a series of physical measurementmodalities such as a laser ultrasonic sensor which could actively exciteor “ping” the deposit with one laser beam and then use a laserinterferometer to measure the resultant ultrasonic waves or “ringing” ofthe structure in order to measure or predict mechanical properties ormechanical integrity of the deposit as it is being built. Additionally,there could be contact sensors 362 on the mechanical device 361, whichspreads the powders. Contact sensors 362 could be accelerometers,vibration sensors, etc. Lastly there could be other types of sensors363. These could include contact sensors such as thermocouples tomeasure macro thermal fields or could include acoustic emission sensorswhich could detect cracking and other metallurgical phenomena occurringin the deposit as it is being built. In some embodiments, one or morethermocouples could be used to calibrate temperature data gathered bysensors 359. It should be noted that the sensors described inconjunction with FIGS. 3A and 3B can be used in the described ways tocharacterize performance of any additive manufacturing process involvingsequential material build up.

FIG. 4 shows a correlation protocol by which various measurements madein different frames of reference can be cross-correlated is shown inFIG. 4. The Lagrangian data 400 is collected in the manner as describedpreviously. The measured peak temperature 401 in the Lagrangianreference frame is recorded at any given instance in time. TheLagrangian peak temperature 401 can be corrected by a correlation factor402 which is derived by comparing the Lagrangian data to the Euleriandata 405 over the given time step and making corrections as required. Bydoing this at multiple locations in the build plane, the correlationfactors 402 may be experimentally derived. The need for such acorrelation factor arises principally for two reasons. First, there area multitude of optical attenuations as the collected light radiated fromthe molten zone makes its way back through the optical train of theLagrangian sensors. This was described in detail in FIG. 3.Additionally, there is an error associated with the view factor from themolten zone back to the scanning head where both the laser light isprojected onto the build plane as well as the radiated light from themolten zone is collected. This view factor varies as the molten zonemoves around on the build platform. Therefore if a variety ofcalibration points are chosen on the build plane where the Euleriansensor could be trained as well as the Lagrangian sensors could collectdata, in this manner a correlation and correction factor matrix 402could be derived.

Then in the next step of the correlation protocol the corrected peaktemperature in the Lagrangian frame 402 is combined with the variousmodels as described previously to make a prediction of the cooling ratesurrounding the molten region and essentially at any point in the buildplane. However, there will inevitably be modeling errors which willdistort this prediction. For example, in order to get a model that canrun in real time, reduced order physics can be used. The use of suchmodels will inevitably result in a loss of fidelity as the runtime speedis traded off for model complexity. Therefore, another set ofcorrelation parameters 404 is needed to correct errors in the predictedcooling rate based purely on Lagrangian data. This is most effectivelydone by again invoking the Eulerian data 405 and comparing it to thepredictions 403 made by the Lagrangian data.

Therefore in this manner, FIG. 4 describes a complete protocol forcorrecting a variety of errors in the Lagrangian data by invokingEulerian data collected over the same regions of space in the buildplane and during the same time interval. By changing the locations atwhich this correlation is made, a representative set of correlationfactors (or even matrices of such correlation factors) and be derived asindicated by 402 and 404 in FIG. 4.

It is instructive to now consider the specific kinds of inferences andprocess conditions which may be tracked by the use of various Lagrangiansensors as well as their correlation to corresponding Eulerian sensors.First, consider the Photodiode sensor, hereinafter abbreviated as PD.The photodiode sensor in the Lagrangian frame of reference measures theintegrated effect of the radiated optical energy coming from the moltenzone and collected by the scanning optical system. The various physicalfactors that will influence the magnitude of the photodiode (PD) signalinclude: size of the molten region; temperature of the view factor;emissivity of the molten region; geometrical view factor between theview factor and the scanning head optics; various optical attenuationsthroughout the optical train in order to reach the PD sensor; and thespectral response of the PD sensor.

Despite all of these physical factors which should be properly accountedfor to interpret the PD signal in an absolute sense, it can neverthelessprovide valuable relative information and could form the basis fordiscerning various process conditions.

As an example, consider variations in power as well as travel speed. Forexample consider the following parametric variations in power and travelspeed for a specific laser powder sintering process.

TABLE 2 Example of Process Variations in Beam Power and Travel SpeedBeam Power, W Travel Speed, mm/s 97.5 400 97.5 800 146.4 400 146.4 600146.4 1000 146.4 1200 195 400 195 800 195 1600

FIG. 5 shows the resulting average PD signal intensity in the Lagrangianreference frame for the process conditions shown in Table 2. It is seenthat the PD signals for 97.5 W beam power 500, 146.4 W beam power 501,and 195 W beam power 502 are all readily distinguishable on the basis ofthe PD signal intensity. If the individual power levels are examined andthe variation with travel speed is more closely studies, then aconsistent trend is discovered. For example FIG. 6 shows the variationin PD Average Voltage signal with travel speed at the power level of146.4 W. At low travel speeds of 400 mm/s, the PD signal is lower thanat slightly higher travel speeds. It should be noted that the lasersintering process is occurring in a powder bed, and that at lower travelspeeds, and it is important to consider various characteristic timesassociated with powder consolidation as they compare to thecharacteristic time of interaction which will go down as the travelspeed goes up. First it should be determined if the powder consolidationmechanism is dominated by either capillary forces or inertial forces.

First, the Capillary time is the time necessary for an interface toregain its equilibrium shape after a perturbation and is given by:

$\begin{matrix}{t_{CAP} = \frac{\eta L}{\sigma}} & {{Eq}.(19)}\end{matrix}$

Where η is the viscosity, L is the characteristic length, and σ is thesurface tension. Similarly the Rayleigh time is defined as the timerequired for the relation of the interface under the action of inertiaand surface tension forces:

$\begin{matrix}{t_{RAY} = \sqrt{\frac{\rho L^{3}}{\sigma}}} & {{Eq}.(20)}\end{matrix}$

Where ρ is the density, L is the characteristic length, and σ is thesurface tension. Typical values for various thermophysical constants forthe titanium alloy Ti-6Al-4V are given in the table below.

TABLE 3 Thermophysical Properties for Ti—6Al—4V (from the Reference byKoerner, Bauereiss and Attar) Physical properties Experiment Density(liquid) 4000 kg m⁻³ Viscosity 0.005 Pas Surface tension 1.65 N m⁻¹Gravitational acceleration 9.81 ms⁻² Thermal diffusivity of solid 7.83 ×10⁻⁶ m² s⁻¹ Thermal diffusivity of liquid 9.93 × 10⁻⁶ m² s⁻¹ Solidustemperature 1878 K Liquidus temperature 1928 K Preheat temperature 1023K Latent heat 0.37 × 10⁶ J kg⁻¹ λ_(abs) 0.4 μm⁻¹ Specific heat 700 Jkg⁻¹ K⁻¹

Using these properties and a characteristic length of 100 microns, weget the following approximate values for the Capillary and Rayleightimes:t _(CAP)=10⁻⁷ /t _(RAY)=10⁻⁴   Eq. (21)

The characteristic beam interaction time for various travel speeds isshown in the table below.

TABLE 4 Characteristic Beam Interaction Time as a Function of TravelSpeed Beam Interaction Time for Travel Speed, mm/s 100 micron length,seconds 400 2.5E−04 600 1.7E−04 800 1.3E−04 1000 1.0E−04 1200 8.3E−051600 6.3E−05

Considering Equation 21 and Table 4, it is apparent that the Capillarytime is much shorter than the Rayleigh time, so the powder consolidationprocess after melting is dominated inertial effects countered by surfacetension as opposed to viscous effects. Secondly, the beam interactiontime for low values of travel speed are of the same order of magnitudeas the Rayleigh time. This accounts for the lower PD emission at lowertravel speeds as the molten particles are rearranging themselves on thesame time scale as the melting process is occurring, and therefore thesurface of the molten region will not be regular or smooth as thisprocess is occurring. From Eq. 8, we find that the characteristic timefor heat conduction is an order of magnitude larger than the Rayleightime. Therefore hydrodynamic phenomena are not limiting theconsolidation of powders after melting, but rather thermal conductivityis the “bottleneck” process.

At higher travel speeds, the beam interaction time now becomes shorterthan the Rayleigh time and much shorter than the heat conduction time.Therefore the energy density per unit length along the path of the laseras expressed in J/mm drops. As this drops, the average temperature ofthe melt pool will cool, and therefore the PD signal will drop withincreasing travel speed once the characteristic beam interaction timehas become smaller than the Rayleigh time. This trend holds true athigher power levels too, and FIG. 7 shows a similar trend at 195 W.

FIG. 8 shows exemplary embodiments in which the photodiode, or PDsignal, could also be used for anomaly detection. For example, considerthe situation of a laser powder bed melting process utilizing a laser asthe intense moving heat source. If there occurs a localized anomaly inthe powder bed such that it momentarily disrupts the PD signal andcauses it to fluctuate, this will result in a signal characteristic thatmay be discernable either in a temporal analysis or a frequency basedanalysis. The physical basis for this fluctuation is that the lightemission characteristics of the source will momentarily change and willcause a disruption to the PD signal. In FIG. 8, both nominal conditionsand off-nominal conditions are shown in A and B respectively. In A, thelaser beam 800 strikes the substrate 801 and produces a melt pool 802.If the surface conditions have no irregularities, then the emitted light803 is radiated in a solid angle equal to 2π a steradians, or the entiretop plane in a uniform manner. In B, there is depicted the hypotheticaloccurrence of a very large powder particle that is in the powder bed andthat significantly skews the melt pool as well as the local geometry. Inthis case the beam 804 strikes the substrate 805 as before, but the meltpool 806 is significantly distorted due to the local geometric anomaly.Therefore the light emitted 807 does not radiate uniformly over the 2πsteradians as before, but rather the radiation distribution is now lessuniform and has asymmetrical properties. This will directly impact theobserved PD signal as the PD signal is nothing more than the collectedlight radiated from the melt pool and collected through the scanninghead optics. Any change in the spatial or temporal distributions of thisradiation will directly impact the PD signal and will therefore resultin an anomaly event that could be detected in the time domain, thefrequency domain, or a combination of both.

Shifting attention to the use of imaging sensors, it is useful toconsider what information may be obtained from such sensors. By imagingsensor in this current work is meant any multi-pixel array of opticalelements capable of detecting light over a range of frequencies.Furthermore an image shall mean any light pattern that is projected onsuch a sensor array through a series of optics such as lenses, mirrors,gratings, etc. it is important to note that the sensor array willprovide both imaging and non-imaging information.

FIG. 9 shows melt pool 900 clearly visible in this single frame from ahigh speed camera that was observing an electron beam melting additivemanufacturing process. Using image processing software, it is possibleto quickly find the outline of the melt pool 901 and therefore toquantify the size of the melt pool. However this is only a top surfaceview and does not always correlate to the depth or volume of the meltpool. Using the same high speed images as shown in FIG. 9, it ispossible to extract non-imaging information from these images. In FIG.10, the same image as shown in FIG. 9 is shown in false colorhighlighting. The melt pool 1000 is again shown as an image that can bemeasured. However, if discretize the entire image and look at the lightintensity within these discrete elements, it is possible to extractnon-imaging information from such data. For example, 1001 is an elementat the edge of the melt pool at one location, and 1002 is another suchelement at another location. If these areas 1001 and 1002 are consideredto be virtual sensors, it is possible to see the intensity level atthese locations and to plot this data as a function of time. Therefore1003 is the time trace corresponding to the intensity data collected atregion 1001, and correspondingly 1004 is the time trace of intensitydata from location 1002.

It is clear from the plots in FIG. 10 that the light intensity collectedfrom different regions of the melt pool image are periodic and exhibitan oscillatory behavior. The direct physical explanation of thisoscillatory behavior is that the melt pool may be considered as adroplet of liquid that has various forces acting on it includinginertial forces as well as surface tension forces. Under the restoringforce of surface tension, any disturbance to the liquid droplet willresult in oscillations and modes of oscillation. The free surface of themelt pool should therefore be in motion due to these oscillations, andany emitted or reflected light would therefore be modulated by theseoscillations. Therefore the signals as measured in FIG. 10 and reflectedin the time traces 1003 and 1004 should contain frequency information onthe melt pool. Additionally, artificial illumination such as that from alaser at a different frequency could be used to accentuate thesespecular reflections from the melt pool that could contain frequencyinformation. The melt pool frequency may be modeled by the followingrelationship:

$\begin{matrix}{t_{n - 1} = \frac{\sqrt{\frac{3{\pi\rho}V}{\,^{\prime}\Upsilon}}}{\sqrt{( {{n( {n - 1} )}( {n + 2} )} )}}} & {{Eq}.(22)}\end{matrix}$

Where n is the mode of oscillation, ρ is the density, V is the volume ofthe melt pool, γ is the surface tension, and t n−1 is the period ofoscillation for the nth mode. In FIG. 11, the dependence of oscillationfrequency as a function of melt pool size is shown for the primary orfirst mode of oscillation. The material selected for this calculationwas titanium, commercially pure. Since the melt pool diameter for theadditive manufacturing processes under consideration will be muchsmaller than 0.5 mm, it is expected that the melt pool frequency will bewell over 1000 Hz. However, there is a practical limit to oscillationsand the ability of the liquid metal to respond on account of attenuationand dispersion of oscillations.

Yet another kind of sensor data that could be examined is data collectedon heating and cooling rates. The most common type of sensor in thiscategory is a pyrometer. The pyrometer could be used in both theLagrangian and Eulerian frames of reference. Also, run-time data fromthe machine tool or measurements from the Lagrangian frame of referencecould be combined with process models to predict quantities such ascooling rate which are not directly observable in the Lagrangian frame.For example, suppose we have a direct measurement of the melt poolradius from the Lagrangian reference frame as described above. Thenusing this real time Lagrangian measurement together with the knownmachine parameters of beam power and travel speed, it is possible tocalculate the cooling rate, i.e. the cooling rate as the beam moves pasta specific point in the powder bed. This is normally a quantity bestmeasured in the Eulerian frame, but with the combined measurement andmodeling approach it is possible to approximate the same quantity usingLagrangian data only.

For example, by combining a melt pool radius measurement with Equation4, we get the predicted cooling rate as shown in FIG. 12. This assumesthat the material is a CoCr alloy commonly used in biomedicalapplications as well as some aerospace applications. In FIG. 12, 1200 isthe predicted cooling rate for a point as close to the weld centerlineas possible. There is one user-adjustable parameter, namely this radiusaway from the centerline at which the temperature is evaluated. Itcannot be zero, because Eq. 4 is a point source solution and thereforethe temperature at r=0 is infinite. Therefore this user-adjustableparameter can be calibrated by looking at the appropriate Eulerian data,as outlined in the flowchart is FIG. 4. The black best fit line 1201 isa linear best fit. In FIG. 12, 1201 appears curved because of the logaxis in time. The best fit equation 1202 shows a slope of −99111 degreesC. per second. In FIG. 12, the user-adjustable parameter has been set tor=1 micrometer. This was possible to establish only by a comparison tothe Eulerian data.

FIGS. 13A-13B show experimental data derived from a Eulerian Sensor. InFIG. 13A, 1300 shows the individual scans of the laser on the powder bedas they enter and exit the field of view of the pyrometer. 1301 shows anindividual scan as observed by the Eulerian pyrometer. The heating andcooling rates are simply derived by taking the slopes of the heating(rising) and cooling (falling) portions of the curve shown in 1301.

FIG. 14 shows specific scan cooling rate values on the vertical axis ofa 3D plot. The scan cooling rate 1400 is shown on the vertical axis ofthis 3D plot. The other two axes of the plot include the scan heatingrate 1401 and the scan peak temperature 1402. There are two groups ofdata shown in FIG. 14. The chief difference between these two operatingconditions is the scan peak temperature 1402. One grouping of datapoints namely 1403, has a lower peak temperature, whereas anothergrouping namely 1404 has a higher peak scan temperature. Both sets ofdata have a scan cooling rate of around 100,000° C. per second. In orderto match the Lagrangian data and model prediction shown in FIG. 12, theradius was set to 1 micrometer so that the predicted data would matchthis experimental data as shown in FIG. 14. This is consistent with themethodology shown in the flowchart of FIG. 4. Now that this parameterhas been set, the relation in FIG. 4 could be used for prediction ofcooling rate using only Lagrangian and machine data. This illustratesone use of Eulerian data to calibrate a model prediction derived fromLagrangian data.

Phase Change Determination/Sensor Calibration

Another way to calculate heating and cooling rates is by identificationof phase changes occurring within a Eulerian sensor field of view.

FIG. 15A shows an exemplary field of view of a pyrometer setup as aEulerian sensor. In particular, a circular field of view 1500 isdepicted. When the temperature field within field of view 1500 isvariable and is a function of location as well as time, then eachindividual area element will contribute to the overall averagedtemperature that is observed in proportion to its area as a fraction ofthe total area of the pyrometer field of view. The small differentialarea is schematically indicated in FIG. 15. The field of view 1500 mayinclude a multitude of smaller areas dA that are radiating. In generalthese areas are dispersed at a given radius R from the center 202 and ata certain angular orientation θ within the field of view of thepyrometer 203. Furthermore, each individual area may have a differentemissivity that is a function of temperature.

FIG. 15B schematically shows the field of view 1500 of the pyrometer andthe track 1504 of thermally affected and melted material caused by themoving heat source. The field of view 1500 of the pyrometer is shown asa circular field of view with a radius R 1502. The region that is heatedand melted by the moving heat source is shown as a rectangular tract1504, which intersects the pyrometer field of view 1500. Generally, thistract will vary in size, area and location where it intersects the fieldof view.

The heat source is not an instantaneous heat source, i.e. it is notinstantly turning on and releasing a finite amount of heatinstantaneously. Rather, the heat source is a moving, continuous heatsource. Different areas within the field of view are constantlyincreasing and decreasing in temperature as the heat source is movingthrough the field of view sweeping out the heated areas 1504. Therefore,the observed temperature should be interpreted as a time-integratedaverage of the time-dependent thermal behavior of the hot and coldregions—each weighted by their area fractions. FIG. 15C shows anexemplary scan pattern 1506 that can be traversed by a heat source. Asdepicted, field of view 1500 can represent a relatively small portion ofscan pattern 1506 and consequently, can only accurately quantify heatingand cooling occurring within field of view 1500. It should be noted thatother scan patterns are possible and can include tighter or looser scanpatterns performed at various speeds with various power outputs. Thescan rate, power and scan pattern all have an effect upon how muchenergy gets delivered during the additive manufacturing operation.

FIGS. 15D-15E show various alternative scan patterns. FIG. 15D shows howthe scan pattern can be broken into smaller checkerboards which arescanned sequentially left to right and top to bottom. The numbers ineach checkerboard indicate the order in which each checkerboard isscanned. In FIG. 15E, the same checkboard pattern is shown, but now thescan order for the individual checkerboards is randomized. Irrespectiveof the specific scanning pattern or scanning strategy involved, it isseen that the laser based process involves short, many short, discretescan lengths with a start and a stop and a path length.

If a scan pattern similar to the ones depicted in FIGS. 15D or 15E werebeing monitored by a stationary/Eulerian photodiode, the data comingback to the photodiode would have many, many individual signals eachrepresenting a given specific scan over a specific path length. It wouldbe useful to separate out all of these signals according to their pathlength, as the apparent intensity of the signal as observed by thephotodiode will be a function of this path length. This is because atthe start of the scan, the photodiode intensity is zero or very smallbecause the laser has just turned on. As the scan proceeds the scangenerally becomes hotter and emits more light, so the photodiodeintensity would increase slightly. There would of course be a naturalrange and scatter in the photodiode raw signal as the light intensityvaries throughout the process due to the very chaotic nature of thelaser/powder interactions as well as the chaotic motion of the moltenmetal and the changing view factor from this small hot spot to thephotodiode.

Ways in which pyrometer data having a field of view substantially largerthan the region that is hot, where the heated region is moving, and howto normalize such data so as to predict true temperature from observedtemperature are discussed below.

FIG. 16A shows the raw data from a fixed (Eulerian) pyrometer asmultiple scans of a heat source pass through the field of view 1500. Asthe moving heat source moves into the field of view of the thermalsensor, there is a rise in the background temperature 1600. Eventuallythe moving heat source is fully within the field of view 1500, and thereare higher temperature, rapid thermal excursions 1601. As the heatsource then fades from the field of view and moves to other regions ofthe material, there will be a slower falling thermal transient 1602. Thetransients in FIG. 16A will have phase changes associated with them aswell, but this is only visible or discernable at closer inspection ofthe more rapid, higher temperature thermal transients 1601.

FIG. 16B shows a closeup of the faster transients from therepresentation shown in FIG. 16B that result from the moving heat sourceintersecting the field of view of the thermal sensor. 1610 shows aspecific peak which represents one instance of a transit event in whichthe heat source passes along a path that extends across the sensor fieldof view (e.g. see path 1504 depicted in FIG. 15B). 1611 is the heatingportion of this curve which in general will be very rapid. 1612 showsthe cooling portion of this curve which will also be rapid, but not asrapid as the on-heating portion. 1613 shows a thermal arrest orinversion, which is associated with a phase change in the material,which in the case of the on-cooling curve would be the liquid to solidtransition.

For thermal measurements in which the field of view of the thermalsensor is larger than the hot region being measured and where there arephase changes, there are two primary intervening factors which should beaccounted for when considering the temperature measurements: (1) theapparent observed temperature will be lower than the actual temperatureof the hot region because the field of view of the thermal instrument isthe temperature from cold regions as well as hot regions; and (2) theemissivity of a liquid will be very different than the emissivity of asolid when considering the case of a liquid—solid phase change.

FIG. 17A shows a graph of spectral emissivity as a function oftemperature for Inconel 718 (a Nickel-Chromium Alloy used in someadditive manufacturing operations). One with ordinary skill in the artshould appreciate that the substantial changes in emissivity depicted byFIG. 17A help show why changes in phase should be accounted for to helpoptically determine an accurate temperature. FIG. 17A shows how theemissivity of the solid 1700 is generally higher than the emissivity ofthe liquid 1701. In this case, the solid was a polished wire, so itsemissivity was low to start with. Oxidized surfaces are expected to havesomewhat higher emissivity. The emissivity of a powder bed is a morecomplex phenomena and takes into account the highly irregular surfacegeometry of power. Powder beds often have a very high emissivity whichcan be experimentally measured. The emissivity can also be calculated bymaking an assumption of spherical powders and assuming that the holesare cylindrical, and solving for the geometrical effects, this referencepredicts the combined emissivity of the surface comprised of solidsurfaces and holes. In general, the total emissivity is thearea-fraction weighted sum of the emissivity of the holes and the solidsurface.

FIG. 17B shows a theoretical calculation of the emissivity of the holes,which could approach a perfect black body. Axis 1710 is the fractionalporosity in the powder bed. 1711 is the effective emissivity of theholes, i.e. the regions that are not occupied by solid material. 1712shows the effect of varying the solid material emissivity also as afunction of the powder porosity. It is seen that the emissivity of thehole regions could be very high. However the total emissivity is an areafraction weighted average of the hole emissivity and the solidemissivity. Consequently, it should be appreciated that the emissivityof the powder can be widely varied by adjusting the average of the holeemissivity and the solid emissivity. Table 4 below along with FIG. 17Cgive examples of how this volume fraction porosity of the powder varieswith respect to effective total emissivity and total solid emissivity.

TABLE 4 Volumetric Porosity vs Effective Total Emissivity TotalEmissivity - Total Emissivity - Total Emissivity - Volumetric 0.5 Solid0.7 Solid 0.9 Solid Porosity Level Emissivity Emissivity Emissivity 0.050.501 0.701 0.900 0.1 0.505 0.703 0.901 0.15 0.513 0.708 0.902 0.2 0.5250.715 0.904 0.25 0.540 0.723 0.906 0.3 0.558 0.733 0.906 0.35 0.5760.741 0.904 0.4 0.591 0.747 0.897 0.45 0.601 0.746 0.883 0.5 0.604 0.7380.862

The additional correction that should be made to data from a thermalsensor in which the hot region is considerably smaller than the field ofview is the area fraction contribution of hot and cold elements to theoverall thermal sensor signal. If there are two objects in the field ofview of the pyrometer and they have different emissivities, temperature,and areas, then the total radiant flux reaching the sensor, assumingthat the Stefan-Boltzmann Law applies, is approximately proportional tothe following quantity:E _(TOTAL)∝ε₁ A ₁ T ₁ ⁴+ε₂ A ₂ T ₂ ⁴   Eq.(23)

If we consider the typical numbers encountered in AdditiveManufacturing, then the ratio of the areas will be approximately 0.01,since a typical spot size for a laser used in AM is on the order of 100microns (0.1 mm) whereas the spot size of the field of view of thepyrometer is closer to 1 mm. For a specific sensor over a specificspectral range, the difference in power emitted at differenttemperatures is even more extreme than that given by theStefan-Boltzmann Law, since this law looks at emission over allfrequencies. For example if we limit emissions to just those occurringbetween wavelengths 1.58 microns to 1.8 microns, then the resultingtemperature vs. power density (assuming a solid angle of π steradians)when plotted in a log plot is shown in FIG. 17D. According to the slopeof the best fit line 1100, we see that over this spectral range the moreappropriate relationship between power density and temperature is:E ∝T^(6.7)   Eq.(24)

The peak temperature will be on the order of 2000K, whereas the basetemperature will be closer to 500-750K. Therefore the ratio of thetemperatures raised to the 6.7 power is approx. in the range 1000-10000.When multiplied by the ratio of the areas, the hot spot is 10-100 timesthe signal intensity for a given emissivity. So the area correctionfactor over the spectral range of the thermal instrument described abovevaries from 1% to 10% depending on the base temperature. Therefore forthis variety of additive manufacturing process and for pyrometers wherethe field of view is 1 mm or less as compared to a 100-200 micron meltpool, the effect of cold regions within the field of view may beignored.

An additional correction that can be made to raw data is an emissivitycorrection. For a given thermal sensor, it will assume an emissivity.This assumed emissivity will in general not be the correct emissivityfor the material over its entire temperature range. For example we havealready seen that on melting there is a dramatic drop in emissivity.Therefore, the temperature can be corrected based on emissivity, andthis is done using the following relationship:

$\begin{matrix}{\frac{1}{T_{NEW}} = {\frac{1}{T_{MEASURED}} + {\frac{\lambda_{EFF}}{C_{2}} \cdot {\ln( \frac{\varepsilon_{NEW}}{\varepsilon_{M}} )}}}} & {{Eq}.(25)}\end{matrix}$

There T_(NEW) is the new temperature at the correct emissivity ε_(NEW),T_(MEASURED) is the measured temperature at the set instrumentemissivity of ε_(M), λ_(EFF) is the effective wavelength of theinstrument, for example it could represent the midpoint of thewavelength range over which the instrument is measuring, and C₂ is thesecond Planck Constant.

FIG. 18 shows a flowchart describing in detail how thermal sensor datafrom various locations in the additive manufacturing build plane can beused to derive features which are useful for quality inference andquality control for the AM process as a whole. At 1800, raw data iscollected from various thermal sensors. The raw data is collected andthem numerically smoothed at 1801 and then differentiated at 1802. Theon-heating portion of the thermal curve will show a break in slope atsome point during the temperature rise. This is caused by melting asmelting is the only physical phenomenon which could account for thisbreak in slope. Therefore at 1803 the maximum in the derivative datawill be used to find the location where melting occurs and this point inthe raw data will be associated with the value of the equilibriumliquidus for the alloy in question. Similarly at step 1804, theon-cooling liquidus will be identified based on a thermal arrestassociated with the solidification process. In general the on-heatingand on-cooling liquidus points will not be the same. At 1805 the rawdata can be converted to temperature data by circuitry of the pyrometerby using a default or assumed emissivity. At 1806 the temperature datais scaled by comparing the measured on heating liquidus to the knownmelting temperature of the material and then using the identifiedon-heating and on-cooling liquidus positions to further adjust thetemperature in accordance with changes in emissivity due to phasechanges. At 1807 a temperature corrected and emissivity correctedthermal curve is produced that can be used to drive features such asheating rate, peak temperature, and cooling rate.

FIGS. 19A-19F illustrate how the process described in FIG. 18 can beapplied to real temperature readings. FIG. 19A shows a raw thermal tracefrom an actual AM process involving the sintering of nickel-basedsuperalloy IN-718 (see FIG. 17A). It shows the raw data 1900 as well asa smoothed curve 1901. In FIG. 19B, the derivative data is similarlyshown, but the y-axis of the derivative has been scaled by 50 to make itmore visible. Similarly 1910 shows the derivative data and 1911 shows asomewhat smoothed version. These are put onto the same plot in FIG. 19Cwhere it can be seen that the maximum 1920 in the derivate occurs attime increment 40. This location on the temperature data will beassigned as a the location of the on-heating liquidus temperature,because after that point the temperature drops as more and more liquidis present and the heat transfer to the surrounding powder bed isgreatly enhanced.

FIG. 19D shows the raw data converted to apparent temperature based onan assumed instrument emissivity setting of 0.9. Also the timeincrements have been converted to time based on a sampling rate of 50kHz. This is now the starting point for corrections that are to be made.First we note that the location of the first derivative maximum isassumed to the liquidus temperature. Now the emissivity correction canbe made. The assumed instrument emissivity was 0.9. For a IN-718 powderbed the more accurate assumption for emissivity is 0.8 So thiscorrection is made for temperatures up to the liquidus temperature. Fortemperatures above that, the emissivity will be assumed to be low,namely 0.4 based on the data from FIG. 17A. The results of thisemissivity correction are shown in FIG. 19E. With the original apparenttemperature 1900 and the corrected apparent temperature 1931. Now thisemissivity corrected temperature is scaled by the liquidus temperaturelocation.

Because the liquidus temperature is assumed to be 1336 deg C. and theapparent temperature at the liquids temperature location is 809 deg C.,the scaling factor becomes 1336/809=1.65. Applying this scaling factorto FIG. 19E provides the results depicted in FIG. 19F. We can now derivea variety of features from this curve that are of significant interestfor the purposes of establishing whether or not the process is undercontrol. 1940 shows the point at which heating starts. 1941 is theliquidus temperature on heating. The heating rate between these twopoints is a feature of significant interest as it is the heating rate upto the melting point of the material on heating. From the data in theFIG. 19F, we find that this heating rate is(1336C−964C)/(0.0008s−0.00034s)=808,695 C/s. The in FIG. 19F we see thatthe peak temperature 1942 is 1610 C. The on-cooling liquidus location isthe location of the first thermal arrest 1803 on cooling, and we seethat from the data in FIG. 19F this temperature is at 1288 C, which issignificantly below the equilibrium liquidus of 1336 C. The solidusfeature 1944 is at a temperature of 1160, which again is below theequilibrium solidus for this alloy. The cooling rate between 1943 and1945 is the most important cooling rate from a metallurgical point ofview as it is the average cooling rate during the alloy solidification.1945 is where the projection of the solidus line hits the cooling curveon cooling. From the data in FIG. 19F, we find that this cooling rate isbetween points (1160C−1288C)/(0.0049s−0.00434s)=228, 571 C/s.

These features, the heating rate the cooling rate, and the peaktemperature as defined above and in reference to FIG. 19F are the mostmetallurgically important features which could be tracked scan by scan,layer by layer. These features could be used to establish a statisticalprocess control methodology and could answer the question, “is theprocess under control?”

Process Control

Process control is a very broad and general topic which could havemultiple meanings and connotations. FIG. 20 shows the hierarchy ofprocess control from the highest level requirements down to the lowestlevel real-time control loop. The Engineering End Use Environment 2000ultimately determines the requirements placed upon any part or system byspecifying the functional and operational conditions under which thepart will be used. This could include but is not limited to operatingtemperature and/or pressure, specific geometric envelopes that the partcan fit into, forces and torques applied on the part, the number ofcycles of oscillating stress that the part will be subjected to over agiven period of time, etc. This is turn drives the definition of DesignIntent 2001. Design Intent 2001 is the quantification of specificmetallurgical, geometric, and mechanical properties and attributes thatthe part must possess so that it could successfully function in theengineering end use environment 2000. Design Intent 2001 could bederived from modeling or from significant prior experience or expertknowledge to predict what part properties and attributes will berequired and to specify those. The Quality Specification 2002 outlinesthe methods and techniques by which the part attributes specified by

Design Intent 2001 will be actually verified or measured. The variousspecific techniques, work instructions, and detailed execution steps arefurther elucidated in the Inspection Protocols and Standards 103 whichcould also include standards and methods for calibration ofnon-destructive or destructive inspection techniques.

The next step in quality assurance, quality control and process controlis the Process Qualification 2004. This is generally a very lengthy stepin which the all the inputs to the manufacturing process aresystematically varied or adjusted, the output of the manufacturingprocess and specifically those attributes called out in the QualitySpecification 2002 are measured according to the techniques outlined inthe Inspection Protocols and Standards 2003, and the results arecompared against the requirements outlined in the Design Intent 2001.This can be a highly iterative, time-consuming, and expensive process asmany parts and large samples may be needed to ensure statisticalrelevance. This is because in the traditional approach to qualityassurance, quality control and process control, the individual sample isa part, and therefore entire parts must be sacrificed during the ProcessQualification 104 to ensure that the manufacturing process is capable ofproducing parts that will meet Design Intent 101 as specified bymeasurable attributes in the Quality Specification 2002 and as measuredby the specific techniques prescribed in the Inspection Protocols andStandards 2003.

Assuming that the Process Qualification 2004 is successful and a set ofprocess inputs, the traditional approach to manufacturing qualityassurance, quality control and process control then attempts to “lockdown” manufacturing processes through the formulation and implementationof a Manufacturing Process Specification 2005 that outlines specificmanufacturing process inputs, parameters, or other conditions thatpurportedly will enable the manufacturing process to perform in aconsistent manner in perpetuity so that the manufacturing processdetermined in the Process Qualification 2004 will produce parts capableof meeting Design Intent 2001 on an ongoing basis.

There are two additional measures which are taken in the traditionalapproach that further try to ensure that the Manufacturing ProcessSpecification 2005 will result in acceptable parts which meet DesignIntent 2001. The implementation of Process Control 2006 in a moregeneral sense consists of specific work instructions, engineeringcontrols to ensure that manufacturing or machine settings cannot alteredonce established, or other administrative controls which preventunauthorized alteration of the manufacturing process. Also, the vastrange of Lean Manufacturing tools as well as Continuous Improvementtools such as mistake-proofing, 5-S, Kaizen, etc. falls into thiscategory, as do the traditional methods of tracking and quantifyingquality such as X-R charts, Pareto charts, etc. These are all generallyand widely construed as Process Control 2006 in the traditional sense.

For some manufacturing processes even in the traditional mindset, suchProcess Control 2006 is found to be deficient and not sufficient toensure that the Manufacturing Process Specification 2005 will alwaysproduce components capable of meeting Design Intent 2001. For thissmaller and more limited set of manufacturing processes, and additionaland final step is taken, namely that of Real Time Process Control 2007.This step involves the sensing of real-time information on-machine, theprocessing of this data in real-time, a decision-making algorithm thatis capable discerning normal or expected states of the process fromthose that are off-nominal or unexpected, and finally a controlmechanisms that allows the inputs to the manufacturing process to beautomatically or perhaps even autonomously adjusted so as to continuallyensure that the output of the manufacturing process is within knownbounds.

In the current approach as described by FIG. 20, there are two aspectswhich are implicitly embodied in the term ‘process control:’ i) is theprocess under control, and ii) the means by which to control theprocess. The first aspect deals with processes that could be consideredunder a statistical state of control, i.e. the process is sufficientlyreproducible that with controlled inputs that have known uncertainty,the process is capable of producing consistent outputs with measurableattributes and quantified uncertainty in those measurements. The secondaspect deals with the procedural, administrative, technical, and machineaspects of controlling a process either through administrativeprocedures, simple open loop controls, or through sensing and activereal time feedback control.

This present invention will address these two aspects of process controlbut will do so in a technical and real-time manner as opposed to aprocedural or administrative manner. The former involves sensing,processing of data, making quality inferences by comparison to astandard, and for the case of real-time control making changes inreal-time to machine parameters or inputs. The former involves settingup machine parameters once and “locking them down” under the assumptionthat static process inputs will result in a state of control. Thisadministrative method may work for other manufacturing processes but isnot well suited to process control for additive manufacturing. Inparticular, small errors in an additive manufacturing process can ruinparts that take many hours to build, making any advantage that can savetime by aborting a ruined part early or adjusting the operation tosalvage an off-nominal part is desirable.

Critical Processing Times and Sampling rate for Real-Time DataAcquisition

The data collection for any real-time assessment of process control orimplementation of real-time control should be commensurate with thetimescale of the physical behaviors being controlled. It is seen thatthe thermal diffusion times are the characteristic times that are thelongest and therefore are rate limiting. The other behaviors areessentially instantaneous in comparison. The characteristic time that isperhaps of greatest interest to the as-deposited microstructure is thesolidification time, or the time over which the transition from liquidto solid occurs. If there are defects, porosity, etc. that is trapped inthe solid from the liquid state, it would occur over this timescale.Therefore this is a timescale that would be important to capture for anyreal-time monitoring system.

The solidification time varies from 10⁻³ seconds to 10⁻⁵. Therefore itis reasonable to assume that a typical time might be in the range of10⁻⁴ seconds. This would correspond to a frequency of 10,000 Hz for thehighest frequency at which the process could change in a manner thatcould directly impact quality. It is also seen that for a large numberof cases of interest, the solidification time in additive manufacturingis on the same order of magnitude as the Rayleigh time for therelaxation of a liquid oscillation in the weld pool . This is criticalas well because if there is some anomaly in the fluid flow behavior ofthe weld pool and if such an anomaly gets “frozen in” by solidification,it would be important to have a data acquisition system capable ofseeing both potential anomalies over both time scales.

The fact that these timescales are of similar order of magnitude isconvenient in that a common sampling rate could be chosen that wouldensure the capture of both phenomena. However according to the NyquistCriterion, it is important to oversample by at least a factor of two interms of the sampling rate required to observe any given physicalbehavior. Therefore the real-time data sampling rate should be at least20,000 Hz and preferably higher. Therefore it is seen that a samplingrate of 50,000 Hz will be sufficient to capture in high fidelity andsufficient detail the three most important timescales: the Rayleigh timefor liquid oscillations, the solidification time over which suchoscillations are trapped in a solid state, and the heat conduction timewhich is the overall “process bottleneck” being the slowest time. Nowthat a proper understanding of the characteristic times and the samplingrates has been established, it is possible to look at specific sensors,sensor configurations, and resultant data collected in the real-timeenvironment so as to answer both questions of whether the process isunder control and how to control the process.

Real-Time Measurements Relevant to Quality

Before any process can be controlled or could be deemed to be under astate of control, measurements that correspond to process attributesthat actually determine quality should be ascertained. FIG. 21 reviewsthe overall derivation of quality specifications and categorizes theresultant measurements that should be incorporated in order to verifyadherence to overall quality specification and hence to the originaldesign intent. The quality specification 2100 is divided into threebroad categories: a metallurgical quality specification 2101, amechanical properties specification 2102, and a geometric qualityspecification 2103. The mechanical properties specification 2102 may notbe directly measurable for additive manufacturing processes inreal-time, but certain metallurgical factors such as the presence orabsence of defects directly impacts mechanical properties as well. Thegeometric properties 2103 should ideally be measured in process and onthe machine as the part is being built as well as post-process and afterany additional thermal processing or heat treatment steps, i.e. bothin-process as well as post process geometry measurements 2106. Uponclose correlation between these in process and post process data sets itwill be possible to utilize the in process data to be predictive of postprocess outcomes and therefore the in process data could be used bothfor ascertaining a state of process control as well as implementing realtime process control.

Going back to the metallurgical quality specification 2101, it in turnconsists of two aspects, namely the presence or absence of defects 2104and the microstructure 2105. Examples of defects include porosity andlack of fusion. Porosity is caused by one of two mechanisms.

First, gas pockets which were in the powder bed can get trapped in thesolid state if they do not have sufficient time to escape from the melt.This is a function of the beam interaction time and the capillary time.Generally at high temperature the viscosity drops off considerably andthe capillary time will be very small. However if the temperaturegradient is large and if the temperatures towards the bottom of the meltpool are lower, it is possible to have a range of effective capillarytimes such that there could be trapping of gas pockets subsurface andinsufficient time for their escape as the beam moves by, even insubsequent re-melt passes. The second mechanism of porosity generationis through material vaporization, either the powder material itself inthe case of many fine particles in the powder particle sizedistribution, or through vaporization of organic or inorganic or otherforeign material that may be in the powders. The mechanism here is thedifferential solubility of gas in liquid vs. solid, and the fact thatthe solubility of gas in solid is generally far lower. As the materialchanges phase form liquid to solid, the gas would then be forced to comeout of solution and would therefore form fine porosity.

With regards to the microstructure 2105, an important governing factoris that additive manufacturing requires a melting and solidificationstep as the key step to form a consolidated macrostructure with a givenmicrostructure. Therefore the thermal history over fast and slowertimescales is the most important governing factor that determines themetallurgical quality 2105.

On the basis of the discussions above it is seen that real-timemeasurements should focus on two broad categories: thermal measurementsover various time scales, and geometric measurements of as-builtgeometry. Both of these will impact mechanical properties, but there isnot a direct measure of material strength, fatigue life, etc. based onreal-time signals alone. Post-process data such as the results ofmechanical properties testing can be conducted to make the in-processreal time data predictive.

There is a direct and deterministic correlation and connection betweenthe underlying thermodynamics of the material system in questions (phasediagram) and the processing history that the material system underwent.In process and real time measurements track the evolution and the stateof the processing history. However these measurements alone withoutadditional phase diagram and thermodynamic guidance may not besufficient to predict mechanical properties. Additional experimental,theoretical, modeling-based, or ab initio information regarding thematerial kinetics and specifically how various equilibrium ornon-equilibrium phases are attained is essential to a full understandingof the mechanical properties. Therefore this invention will focus on twosets of real time measurements as predictive of quality (necessary butperhaps not completely sufficient to fully specific quality):

-   -   Thermal measurements over various timescales    -   Geometric measurements in real-time and in process.        Thermal Measurements and Metallurgical Quality

Generally, there are many different possible in-process physicalmeasurements that could be performed on a manufacturing process. Some ofthese are listed in the table below which is a representative but notcomplete or exhaustive listing.

SENSOR PHYSICAL BEHAVIORS IT COULD CATEGORY SENSORS MEASURE Force andAccelerometers The uniformity of the powder addition process vibrationvibration sensors which typically involves a mechanical arm thatmechanical shock sensors spreads the powders. Any irregularities in thestrain gages arm, the mechanical motion, the spreading action,piezoelectric sensors or the arm hitting previously deposited layerscould be important to indicate possible non- uniformities in the powderbed as a result of errors in this mechanical spreading action. Contactthermocouples The powder bed temperature as well as other thermalthermistors temperatures in the equipment, the processing resistancethermal detectors (RTDs) chamber, or other aspects of the manufacturingprocess could be sensed and detected with these sensors. This kind ofdata is valuable to know the macro thermal state of the process as wellas for machine diagnostics and preventative maintenance for the machineitself. Non-contact single color pyrometer These sensors measure bothprocess as well as thermal two or multi-color pyrometer ambient powderbed temperatures and could do so thermal imaging camera in the frame ofreference of the laser or in a ratio pyrometers stationary referenceframe. They can measure fiber optic pyrometers very fast thermaltransients associated with heating, melting and cooling as well asslower thermal transients at longer timescales as was discussedpreviously. Optical photodiode These sensors could again be in a movingor a spectrometer fixed reference frame. Photodiodes measure intensityof light emissions over a given range of wavelengths and could becorrelated to such features as weld pool size and/or temperature. Theycould also detect anomalies such as regions where the laser powerabsorption suddenly changes, or areas where the power absorbed otherwisefluctuates. Spectrometers can also perform chemical analysis of thevaporized and either ionized or unionized plasmas or vapors associatedwith the additive manufacturing process Optical high speed camera Thesetypes of sensors could be used again in the camera frame of reference ofthe beam or in a stationary linear camera frame. They could measure suchthings as weld other optical imaging systems pool size and shape, theshape and precise metrology of the layer just deposited, irregularitiesin the manufacturing process, the integrity of the powder bed as newpowder layers are applied, as well as other nominal and off-nominalprocess conditions Other laser ultrasonic This category of sensorsinvolves other or eddy current multiple physical phenomena. For examplethe ultrasonic laser ultrasonic could involve a laser acoustic emissioninterferometer which could directly interrogate the manufacturingprocess, or in conjunction with an excitation source could be used todirectly measure mechanical properties of the deposit as the processbuild is occurring. Eddy current sensors can similarly measure theintegrity of the build if they are swept over the build up part.Similarly it may be possible to perform in-situ ultrasonic measurements.Acoustic emission measurements may be sensitive to high speedmetallurgical phenomena such as dislocation motion and cracking andwould be attached to the base of the part being built up

For the purposes of this present invention, the range of sensors will belimited to those which can measure thermal phenomena. Also, thesesensors could be in a moving frame of reference with respect to the beam(i.e. moving with the beam as it scans) or they could be in a stationaryframe of reference. These two frames are more commonly known asLagrangian or Eulerian respectively. Exemplary embodiments showing theabove sensors arranged in an additive manufacturing environment havebeen depicted in FIGS. 3A and 3B.

Control Types and Examples

The following three types of process control can be applied to thedescribed processes based at least in part upon the calibratedtemperature data depicted in FIG. 19F: (1) Process Intervention, or thestopping or interruption of a process for cause based on one or morecritical process features going out of a specified range; (2) Interlayerprocess control, or the alteration of process parameters between layersin an additive manufacturing process based on measurements made duringthe penultimate layer, quality or feature metrics calculated from suchmeasurements, and a decision algorithm which decides if these featuresare within specified ranges and if they are not then how to makeadjustments to process parameters such as heat source power and travelspeed to get these features or quality metric back into their specifiedranges; and (3) Intra-layer, or scan-level process control, in whichpower, travel speed or other process parameters could be changed so thatcertain quality metrics or features will remain within specified ranges.

The third form of process control is the fastest and requires thefastest control loop. The first form of process control may be viewed asan open loop control with only one outcome, i.e. the process is haltedwhen conditions are seen to drift too far from nominal. The second formis a slower form of real time control and only adjusts parameters on alayer by layer basis.

FIG. 22 shows a high level process control flow chart which utilizes theprocess features discussed previously. This diagram shows the processflow for the case of intra-layer or scan by scan control. In suchcontrol, a single scan is conducted, calculations are made, and ifnecessary adjustments are made prior to the next scan. This is thereforeenvisioned as a fast control loop which makes changes in a millisecondor potentially less. At 2200 thermal measurements are taken using avariety of thermal sensors. These thermal measurements are correctedaccording to the process flow chart shown in FIG. 18. Then features areextracted such as those discussed above which could include, but are notlimited to, such features as the heating rate up to the liquidustemperature on heating, the peak temperature, and the cooling ratebetween the liquidus and solidus on cooling. These are features thathave metallurgical significance for the material and the as-depositedadditive manufacturing buildup.

Then at 2203, it is seen whether or not these features are within theprescribed ranges that are known to correspond to nominal processbehavior and are known to produce acceptable parts. If the answer isyes, then at 2204 the process continues to the next scan with the sameprocess variables/process parameters. Note that there could be hundredsor thousands of scans within a single layer of an AM part, and therecould be thousands of such layers per part. If the result of the queryposed in 2203 is no, then at 2205 the process flow is diverted to adecision at 2206. At 2206, some methodology that can make a decisionbased on the magnitude and direction of the deviations observed isapplied. This decision logic could be a reduced order process model, orit could be a lookup table or database, or it could be some heuristicscheme like a neural network, or it could be any other algorithmicsystem that decides which process variables or process parameters tochange, by how much, and in which direction (increase or decrease). Forexample, a change in process variables or process parameters can takethe form of changes to the heat source heat output power, travel speedand scan pattern, which can alter the amount of energy introduced to oneor more layers of a part. Then at step 2207 these new process parametersare utilized to make the next scan based on the data provided by thepenultimate scan, and the process is repeated until the layer andultimately the part is completed. Generally, increases in power anddecreases in heat source travel speed result in greater amounts of heatbeing added to the part. By adding greater amounts of heat, thesolidification rate actually decreases. So, to fix a condition in whichsolidification is occurring too rapidly, additional heat can be added tothe system. Conversely, if solidification of the materials are happeningtoo slowly, then an amount of energy delivered to the part can bereduced, which increases the rate at which solidification occurs.Generally speaker the rate at which the material solidifies is quiteimportant as cooling rates too far out of bounds tend to degrade thequality of the finished part. Another way to adjust the amount of heatdelivered to a particular layer or area is by adjusting the scanpattern. For example, a scan pattern with passes grouped closelytogether would deliver relatively more heat to the part than anotherlaser otherwise using the same settings but with a broader scan pattern.

The various aspects, embodiments, implementations or features of thedescribed embodiments can be used separately or in any combination.Various aspects of the described embodiments can be implemented bysoftware, hardware or a combination of hardware and software. Thedescribed embodiments can also be embodied as computer readable code ona computer readable medium for controlling manufacturing operations oras computer readable code on a computer readable medium for controllinga manufacturing line. The computer readable medium is any data storagedevice that can store data which can thereafter be read by a computersystem. Examples of the computer readable medium include read-onlymemory, random-access memory, CD-ROMs, HDDs, DVDs, magnetic tape, andoptical data storage devices. The computer readable medium can also bedistributed over network-coupled computer systems so that the computerreadable code is stored and executed in a distributed fashion.

The foregoing description, for purposes of explanation, used specificnomenclature to provide a thorough understanding of the describedembodiments. However, it will be apparent to one skilled in the art thatthe specific details are not required in order to practice the describedembodiments. Thus, the foregoing descriptions of specific embodimentsare presented for purposes of illustration and description. They are notintended to be exhaustive or to limit the described embodiments to theprecise forms disclosed. It will be apparent to one of ordinary skill inthe art that many modifications and variations are possible in view ofthe above teachings.

What is claimed is:
 1. An additive manufacturing method, the methodcomprising: depositing a layer of metal material on a build plane;melting a build region of the layer of metal material using a heatsource that scans across the build region, wherein the heat sourcegenerates a weld pool where the heat source interacts with the layer ofmetal material; monitoring a fixed area of the build region with a firstsensor; monitoring a portion of the build region with a second sensorhaving a moving field of view that is aligned with the heat source; andcalibrating the second sensor with data acquired from the first sensorwhen the weld pool is within the fixed area; wherein calibrating thesecond sensor is based on detecting, with the first sensor, a phasechange of the weld pool within the fixed area.
 2. The method of claim 1wherein the data acquired from the first sensor identifies a phasechange of the metal material as the weld pool moves across the fixedarea.
 3. The method of claim 2 wherein the phase change is a liquid tosolid phase change.
 4. The method of claim 3 wherein the liquid to solidphase change is identified by locating a thermal arrest in the dataacquired from the first sensor.
 5. The method of claim 3 wherein atemperature of the phase change is used to calibrate the second sensorsuch that the second sensor reports a similar temperature as the firstsensor.
 6. The method of claim 1 wherein the first sensor is a pyrometerand the second sensor is a photodiode.
 7. An additive manufacturingsystem, comprising: a moving heat source configured to direct energyinto a layer of powder within a build region, the heat source generatinga transient weld pool where the heat source impinges the powder; a firstoptical sensor configured to acquire first sensor data associated with afixed portion of the build region when the heat source generates thetransient weld pool in the fixed portion; a second optical sensorconfigured to move with the heat source and acquire second sensor dataassociated with the build region; and a processor configured to: receivefirst optical sensor data and second optical sensor data when the heatsource generates the transient weld pool in the fixed portion of thebuild region; and calibrate the second optical sensor using the firstsensor data; wherein calibrating the second optical sensor is based ondetecting, with the first optical sensor, a phase change of thetransient weld pool within the fixed portion of the build region.
 8. Thesystem of claim 7 wherein the calibrating results in the second opticalsensor generating calibrated second sensor data that matches the firstsensor data.
 9. The system of claim 7 wherein the processor analyzes thefirst sensor data to identify a phase change temperature of the fixedportion.
 10. The system of claim 9 wherein the phase change is a liquidto solid phase change.
 11. The system of claim 10 wherein the liquid tosolid phase change is identified by locating a thermal arrest in thefirst sensor data.
 12. The system of claim 9 wherein a temperature ofthe phase change is used to calibrate the second optical sensor suchthat the second optical sensor reports a similar temperature as thefirst optical sensor in the fixed portion.
 13. The system of claim 7wherein the first optical sensor is a pyrometer and the second opticalsensor is a photodiode.
 14. An additive manufacturing method, the methodcomprising: depositing a layer of metal powder on a build plane; movinga heat source across a processing region of the layer of metal powder,the heat source creating a moving molten region that fuses the layer ofmetal powder within the processing region; recording first sensor datafrom a first sensor configured to receive optical input from a fixedportion of the processing region; recording second sensor data from asecond sensor configured to receive optical input from the moving moltenregion as it moves across the processing region; and comparing the firstsensor data to the second sensor data when the moving molten region iswithin the fixed portion of the processing region to determine anadjustment, wherein when the adjustment is applied to the second sensordata a result aligns with the first sensor data, wherein the adjustmentis determined based on detecting a phase change of the layer of metalpowder in the fixed portion of the processing region within the firstsensor data.
 15. The method of claim 14 wherein the adjustment is acorrelation factor.
 16. The method of claim 14 wherein the adjustment isa correlation and correction factor matrix.
 17. The method of claim 14wherein the phase change is a liquid to solid phase change.
 18. Themethod of claim 17 wherein the liquid to solid phase change isidentified by locating a thermal arrest in the first sensor data. 19.The method of claim 14 wherein the first sensor is a pyrometer and thesecond sensor is a photodiode.